Hot Technologies with Dr. Claudia Imhoff, Dr. Robin Bloor and SAS Live Webcast on Jan. 14, 2015 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9431631f43a8c7561f2ba996750a4612 When resources are scarce, organizations focus heavily on keeping processes intact and costs down. The result is often a cycle of decisions that hinders development and ultimately leads to zero innovation. But these days, the market is teeming with game-changing solutions with more attractive price points, paving the way toward a new mindset and an era of abundance. Register for this episode of Hot Technologies to learn from veteran Analysts Claudia Imhoff and Robin Bloor as they discuss how the proliferation of data and analytics is forcing the enterprise to rethink and redesign its architecture. They’ll be briefed by Gary Spakes of SAS, who will explain his company’s approach to Big Data analytics. He will show how disruptive technologies like Hadoop can give organizations the scalability and reliability they need, and at the same time boost data discovery, analytic innovation and time-to-value. Visit InsideAnalysis.com for more information.

1. Grab some
coffee and
enjoy the
pre-show
banter
before the
top of the
hour!

2. H T
Technologies
of
2015

3. HOST:
Eric
Kavanagh

4.
THIS
YEAR
is…

5. ANALYST:
Dr.
Claudia
Imhoff
CEO,
Intelligent
Solutions
ANALYST:
Dr.
Robin
Bloor
Chief
Analyst,
The
Bloor
Group
GUEST:
Gary
Spakes
Senior
Manager,
SAS
THE
LINE
UP

6. INTRODUCING
Dr.
Claudia
Imhoff

7. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
President, Intelligent Solutions, Inc.
Founder, Boulder BI Brain Trust (BBBT)
A thought leader, visionary, and practitioner,
Claudia Imhoff, Ph.D., is an internationally
recognized expert on analytics, business
intelligence, and the architectures to support
these initiatives. Dr. Imhoff has co-authored five
books on these subjects and writes articles
(totaling more than 150) for technical and
business magazines.
She is also the Founder of the Boulder BI Brain
Trust (BBBT), an international consortium of
independent analysts and experts. You can
follow them on Twitter at #BBBT or become a
subscriber at www.bbbt.us.
Email: claudia@bbbt.us
Phone: 303-444-6650
Twitter: Claudia_Imhoff
Claudia Imhoff
7

8. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Agenda
§ Extending the Data Warehouse Architecture
8

9. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
A Complex BI Environment
9
Multiple user devices
Multiple output formats
Multiple deployment options
Sophisticated analytics
+ complex analytic workloadsMultiple data sources
Increasing data volumes
& data rates
DW historical
data
Web & social
content
Sensor
data
Operational
data
Text &
media files
Decision
management
Data
management
Data
integration
Data
analysis
Decision
management
Slide compliments of Colin White – BI Research, Inc.

10. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
The Extended Data Warehouse
Architecture (XDW)
10
Traditional EDW
environment
Investigative computing
platform
Data
refinery
Data integration
platform
Analytic tools & applications
Operational real-time environment
RT analysis engine
Other internal & external
structured & multi-structured data
Real-time streaming data
Operational systems
BI services Slide created by Colin White – BI Research, Inc.

11. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Use Case: Real Time
Operational Environment
Embedded or callable BI
services:
§ Real-time fraud detection
§ Real-time loan risk assessment
§ Optimizing online promotions
§ Location-based offers
§ Contact center optimization
§ Supply chain optimization
Real-time analysis engine:
§ Traffic flow optimization
§ Web event analysis
§ Natural resource exploration
analysis
§ Stock trading analysis
§ Risk analysis
§ Correlation of unrelated data
streams (e.g., weather effects on
product sales)
11
Operational real-time environment
RT analysis platform
Other internal & external
structured & multi-structured data
Real-time streaming data
Operational systems
RT BI services

12. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Data Provisioning Use Case:
Data Integration
12
§ Heavy lifting process of extracting,
transforming to standard format
and loading structured data –
mostly batch
§ Physically consolidates data into
“trusted” EDW sets for analysis
§ Invokes data quality processing
where needed
§ Employs low-cost hardware and
software to enable large data
volumes to be combined and stored
§ Requires more formal governance
policies to manage data security,
privacy, quality, archiving and
destruction
Traditional EDW
environment
Investigative computing
platform
Data
refinery
Data integration
platform

13. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Data Provisioning Use Case:
Data Refinery
13
§ Ingests raw detailed structured and
unstructured data in batch and/or
real-time into a managed data store
§ Distills data into useful business
information and distributes the
results to downstream systems
§ May also directly analyze certain
types of data
§ Also employs low-cost hardware
and software to enable large
amounts of detailed data to be
managed cost effectively
§ Requires (flexible) governance
policies to manage data security,
privacy, quality, archiving and
destruction
Traditional EDW
environment
Investigative computing
platform
Data
refinery
Data integration
platform

14. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Traditional EDW Use Cases
14
Most BI environments today
§ New technologies can be
incorporated into the EDW
environment to improve
performance, efficiency & reduce
costs
Use cases
§ Production reporting
§ Historical comparisons
§ Customer analysis (next best offer,
segmentation,
life-time value scores,
churn analysis, etc.)
§ KPI calculations
§ Profitability analysis
§ Forecasting
Traditional EDW
environment
Data
refinery
Data integration
platform
Analytic tools & applications
Operational real-time environment
RT analysis engineOperational systems
BI services

15. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Investigative Computing Use
Cases
New technologies used here
include:
§ Hadoop, in-memory computing,
columnar storage, data
compression, appliances, etc.
Use cases
§ Data mining and predictive
modeling for EDW and real-
time environments
§ Cause and effect analysis
§ Data exploration ( Did this ever
happen? How often? )
§ Pattern analysis
§ General, unplanned
investigations of data
15
Data
refinery
Data integration
platform
Analytic tools & applications
Operational real-time environment
RT analysis engine
Investigative computing
platform
Operational systems
BI services

16. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
All Components Must Work Together
16
analytic models
analyses
New sources of data Enterprise DW
Analytic tools
Investigative
computing platform
Data refinery Operational systems
existing
customer
data
next best
customer offer
3rd party data
location data
social data
feedback
RT analysis engine
call center dashboard
or web event stream
Slide created by Colin White – BI Research, Inc.

17. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Four Forms of Analytics
Based on Delen, Dursun and Demirkan, Haluk, “Decision Support Systems, Data, information and analytics as services,”
from Elsevier, published online May 29, 2012
Business Analytics
Descriptive
(Reactive)
Prescriptive
(Proactive)
Predictive
(Proactive)
What happened?
What is happening?
• Business reporting
• Dashboards
• Scorecards
• Data warehousing
Well-defined
business problems
and opportunities
What will happen?
• Data mining
• Text mining
• Web/media mining
• Forecasting
Accurate projections
of the future states
and conditions
What should I do?
Why should I do it?
• Optimization
• Simulation
• Decision modeling
• Expert systems
Best possible
business decisions
and transactions
OutcomesEnablersQuestions
Diagnostic
(Reactive)
Why did it happen?
• Behavioral analysis
• Cause and effect
analysis
• Correlations
Cause and effects of
changes in business
activities
17

18. Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
Enterprises Must Evolve Their
Analytical Thinking
§ Select few
§ IT managed
§ Reflecting the business
§ What & why?
§ Within the four walls
§ Command/control
§ Discrete activities
§ Configured
§ A conscious thought
§ Tactical necessity
Expanding toFrom
§ Empowered many
§ Business led
§ Driving the business
§ What could & should?
§ The world around us
§ Sense/respond
§ Embedded everywhere
§ Composed
§ In everything we do
§ Strategic advantage
*From IBM
18

19. INTRODUCING
Dr.
Robin
Bloor

20. Robin Bloor, PhD

21. It’s Not Really About Analytics…
There’s nothing new in Data Science
Nearly all the mathematical techniques
have been known for decades – some for
centuries
SO WHY IS THERE SO MUCH EXCITEMENT?

22. It’s About Disruption

23. § Cloud deployments
§ Multicore chips
§ CPU/GPU merging
§ Commodity servers
§ Commodity storage
§ On-chip processing
§ Memory-based
architectures
§ Virtual networks
It’s About Disruption

24. § Cloud deployments
§ Multicore chips
§ CPU/GPU merging
§ Commodity servers
§ Commodity storage
§ On-chip processing
§ Memory-based
architectures
§ Virtual networks
§ Massively scalable software
§ Hadoop + the key-value
revolution
§ Schema on read
§ Marshalling unstructured
data
§ Data availability and the
market for data
§ Event data
§ Big data tools and
architecture
It’s About Disruption

25. u Moore’s Law gave us a
10x speed increase
every 6 years
u Technology disruption is
now giving us a 1000x or
more speed increase
whenever we want it –
as long as we make
sensible technology
selections
u This impacted analytics
first because that’s
where the biggest
workloads were
It’s About Speed

26. The Industrialization of Data
Hadoop
(Staging
Area)
Data
Assaying
Servers
The
Cloud
Desktops
Mobile
Devices
IoT
Data
Exploration
Data
Capture
The Prospecting Domain
Real-Time
Actioning
Data
Management
Hadoop
Archive
Data
Serving
Data
Life Cycle
Mgt
System
Management
Apps
Apps
Apps
Data
Store
Data
Store
Data
Stores

27. u We can speed up all the
technologies in the end-to-
end data chain
u Data analytics that took
days can now take minutes
u Analytics that took months
can be done in hours
u We can process data in
flight
u So it’s not about re-thinking
analytics, it’s about re-
thinking how we use it
It’s About a Much Bigger Data Universe

28. It’s About “Different” Analytics
u Our human control system
works at different speeds:
• Operational control
• Almost instant reflex
• Considered response
u Organizations will
gradually implement
similar control systems
u This suggests a data-flow-
based architecture

29. The Corporate Nervous System(s)
u Mentation
• The Brain
u Fight or Flight
• Sympathetic Nervous
System
u Operational Control
• Enteric Nervous System
• Parasympathetic Nervous
System
Note that these three systems
integrate. It would be bad
news if they didn’t.

30. The New World of Analytics
Ultimately, this is the direction we are
heading in
The speed barriers have been torn
asunder
NOW WE HAVE TO BUILD IT

31. INTRODUCING
Gary
Spakes

33. The
Archive
Trifecta:
• Inside
Analysis
www.insideanalysis.com
• SlideShare
www.slideshare.net/InsideAnalysis
• YouTube
www.youtube.com/user/BloorGroup
THANK
YOU!